Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this p... || attention/attentive model； review information; explicit feature hierarchy (hierarchical); Microsoft Concept Graph; 雇人验证算法解释性好坏；算法鲁棒性robust验证（对用户分组验证结果不变）; || Jingyue Gao; Xing Xie...
In modern e-commerce, the temporal order behind users’ transactions implies the importance of exploiting the transition dependency among items for better inferring what a user prefers to interact in “near future”. The types of interaction among items are usually divided into individual-level interac... || 物品和物品之间的关系通过weight表示出来（解释）；residual network ResNet; 讨论了temporary order在同一个session中的不重要性；attention model weight新求法； Yelp; Amazon; Movies&TV; CDs&Vinyl; || Lu Yu
Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item... || 通过gate neural network自动学习加权参加；为边建立vector representation；类似于OD pair一样将graph中的边也表示成vector；user-user; item-item; user-item; || Liang Hu...
Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users’ actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional... || explicit time interval; time signal; LSTM; Phased LSTM; 融入时间信息； time interval2vec; || Yu Zhu...
Multi-modal transportation recommendation has a goal of recommending a travel plan which considers various transportation modes, such as walking, cycling, automobile, and public transit, and how to connect among these modes. The successful development of multi-modal transportation recommendation sys... || trans2vec; metric learning; graph; embedding; ; || Hao Liu, Ting Li, Renjun Hu, Yanjie Fu, Jingjing Gu, Hui Xiong...
Recommendation systems play a vital role to keep users engaged with personalized content in modern online platforms. Deep learn- ing has revolutionized many research fields and there is a recent surge of interest in applying it to collaborative filtering (CF). However, existing methods compose deep ... || Augmented; Memory Network; attention model; || Travis Ebesu； Bin Shen； Yi Fang...
Providing explanations in a recommender system is getting more and more attention in both industry and research communities. Most existing explainable recommender models regard user preferences as invariant to generate static explanations. However, in real scenarios, a user’s preference is always dy... || CNN; GRU; Time-aware GRU； from static to dynamic explainable； attention mechinism; 权重可视化 visualization；可解释； interpret; Neural Attentive Model for Explainable Recommendation by Learning User Dynamic Preference; || Xu Chen； Yongfeng Zhang...
Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are inde- pendently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same ... || encoder; decoder; content-based; cold-start; Symmetric recovery; projection; lowe-rank; sparsity; || Jingjing Li...
Recent progress in deep learning is revolutionizing the health- care domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health his... || Memory Network; Graph model; RNN; EHR; DDI; longitudinal hidden state; 图模型结合Memory network模型; || Junyuan Shang...
As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, cross-domain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or i... || content-based; review-based; score-based; SDAE; aSDAE; deep learning; cross-domain; || Wenjing Fu...
